Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade.

Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies. • ECG-based age and gender estimation methods are comprehensively reviewed and analyzed. • Methods are stratified based on the type of methodology adopted and the nature of the study (conventional/deep-learning-based and technical/application). • Tabular summaries and textual descriptions of each method are provided, including core methodology, dataset information, key results, and limitations. • Key commonalities and limitations across the reviewed methods are analyzed and presented. • Essential technical aspects of deep learning methods for ECG-based age and gender estimation that need validation for further progress are discussed. • Valuable insights and recommendations for future research and progress are shared. [ABSTRACT FROM AUTHOR]